This package is considered a duplicate. The official version of this package is found at:https://luca-scr.r-universe.dev/ppgmmga

Package: ppgmmga 1.3.1

Luca Scrucca

ppgmmga: Projection Pursuit Based on Gaussian Mixtures and Evolutionary Algorithms

Projection Pursuit (PP) algorithm for dimension reduction based on Gaussian Mixture Models (GMMs) for density estimation using Genetic Algorithms (GAs) to maximise an approximated negentropy index. For more details see Scrucca and Serafini (2019) <doi:10.1080/10618600.2019.1598871>.

Authors:Alessio Serafini [aut], Luca Scrucca [aut, cre]

ppgmmga_1.3.1.tar.gz
ppgmmga_1.3.1.tar.gz(r-4.7-arm64)ppgmmga_1.3.1.tar.gz(r-4.7-x86_64)ppgmmga_1.3.1.tar.gz(r-4.6-arm64)ppgmmga_1.3.1.tar.gz(r-4.6-x86_64)
ppgmmga_1.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
ppgmmga/json (API)
NEWS

# Install 'ppgmmga' in R:

install.packages('ppgmmga', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/luca-scr/ppgmmga/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

On CRAN:

openblas cpp

2.70 score 8 scripts 520 downloads 14 exports 25 dependencies

Last updated from:6bc808e6a8. Checks:6 OK. Indexed: no.

Target	Result	Time
linux-devel-arm64	OK	134
linux-devel-x86_64	OK	146
source / vignettes	OK	237
linux-release-arm64	OK	136
linux-release-x86_64	OK	183
wasm-release	OK	125

Exports:EntropyGauss EntropyGMM EntropyMC EntropySOTE EntropyUT EntropyVAR logsumexp nclass.numpy plot.ppgmmga ppgmmga ppgmmga.options print.ppgmmga print.summary.ppgmmga summary.ppgmmga

Dependencies:cli codetools cpp11 crayon farver foreach GA ggplot2 glue gtable isoband iterators labeling lifecycle mclust R6 RColorBrewer Rcpp RcppArmadillo rlang S7 scales vctrs viridisLite withr

A quick tour of ppgmmga

Alessio Serafini, Luca Scrucca

Rendered fromppgmmga.Rmdusingknitr::rmarkdownon May 21 2026.

Last update: 2023-11-18
Started: 2018-10-14

Citation

Readme and manuals

Help Manual

Help page	Topics
Projection pursuit based on Gaussian mixtures and evolutionary algorithms for data visualisation	ppgmmga-package
Compute the Number of Classes for a Histogram	nclass.numpy
Plots the data onto the projection subspace estimated by the ppgmmga algorithm	plot.ppgmmga
Projection pursuit based on Gaussian mixtures and evolutionary algorithms for data visualisation	ppgmmga print.ppgmmga
Class ''ppgmmga''	ppgmmga-class
Default values for 'ppgmmga' package	ppgmmga.options
Summary for projection pursuit based on Gaussian mixtures and evolutionary algorithms for data visualisation	print.summary.ppgmmga summary.ppgmmga